A Convolutional Neural Network (CNN) is a powerful machine learning technique from the field of deep learning. CNNs are trained using large collections of diverse images.
From these large collections, CNNs can learn rich feature representations for a wide range of images. These feature representations often outperform hand-crafted features such as HOG, LBP, or SURF.
An easy way to leverage the power of CNNs, without investing time and effort into training, is to use a pretrained CNN as a feature extractor.In this Deep learning project, images from a Caltel Dataset are classified into categories using a multiclass linear SVM trained with CNN features extracted from the images.
This approach to image category classification follows the standard practice of training an off-the-shelf classifier using features extracted from images